mcp-github-trending
mcp-github-trending MCP 서버
간단한 API 인터페이스를 통해 GitHub 트렌드 저장소와 개발자 데이터에 대한 액세스를 제공하는 MCP 서버입니다.
특징
GitHub 트렌드 저장소 및 개발자 데이터에 액세스하세요
프로그래밍 언어로 필터링
기간별 필터링(일별, 주별, 월별)
음성 언어로 필터링
잘 포맷된 JSON 응답을 반환합니다.
Related MCP server: ossinsight-mcp
도구
서버는 다음 도구를 구현합니다.
get_github_trending_repositories
다음 매개변수를 사용하여 GitHub에서 인기 있는 저장소를 가져옵니다.
language(선택 사항): 저장소를 필터링할 프로그래밍 언어(예: "python", "javascript")since(선택 사항): 저장소를 필터링할 기간("매일", "매주", "매월")입니다. 기본값은 "매일"입니다.spoken_language(선택 사항): 저장소를 필터링할 음성 언어
응답 예시:
지엑스피1
get_github_trending_developers
다음 매개변수를 사용하여 GitHub에서 인기 있는 개발자를 가져옵니다.
language(선택 사항): 필터링할 프로그래밍 언어(예: "python", "javascript")since(선택 사항): 필터링할 기간("매일", "매주", "매월"). 기본값은 "매일"입니다.
응답 예시:
[
{
"username": "developer",
"name": "Developer Name",
"url": "https://github.com/developer",
"avatar": "https://avatars.githubusercontent.com/u/123456",
"repo": {
"name": "repository-name",
"description": "Repository description",
"url": "https://github.com/developer/repository-name"
}
}
]설치
필수 조건
파이썬 3.12
설치 단계
패키지를 설치하세요:
pip install mcp-github-trending클로드 데스크톱 구성
MacOS의 경우:
~/Library/Application\ Support/Claude/claude_desktop_config.jsonWindows의 경우:
%APPDATA%/Claude/claude_desktop_config.json{
"mcpServers": {
"mcp-github-trending": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-github-trending",
"run",
"mcp-github-trending"
]
}
}
}{
"mcpServers": {
"mcp-github-trending": {
"command": "uvx",
"args": [
"mcp-github-trending"
]
}
}
}개발
건축 및 출판
종속성 동기화 및 잠금 파일 업데이트:
uv sync패키지 배포 빌드:
uv buildPyPI에 게시:
uv publish참고: 환경 변수나 명령 플래그를 통해 PyPI 자격 증명을 설정하세요.
토큰:
--token또는UV_PUBLISH_TOKEN사용자 이름/비밀번호:
--username/UV_PUBLISH_USERNAME및--password/UV_PUBLISH_PASSWORD
디버깅
최상의 디버깅 환경을 위해 MCP Inspector를 사용하세요.
npm 을 통해 MCP Inspector를 실행합니다.
npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-github-trending run mcp-github-trending검사기는 디버깅을 시작하기 위해 브라우저에서 액세스할 수 있는 URL을 표시합니다.
특허
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
Available Tools
2 toolsget_github_trending_developersC
Get trending developers on github
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Language to filter repositories by | |
| since | No | Time period to filter repositories by | |
| spoken_language | No | Spoken language to filter repositories by |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the action without disclosing behavioral traits like rate limits, authentication needs, or output format. It's a read operation implied by 'Get', but details on pagination, error handling, or data freshness are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, front-loading the core purpose. It's appropriately sized for a simple tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects or return values, leaving gaps in understanding how the tool behaves and what results to expect, which is inadequate for a tool with parameters and no structured output info.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional meaning beyond implying filtering for 'trending developers', which aligns with the schema but doesn't enhance understanding. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get trending developers on github' clearly states the verb ('Get') and resource ('trending developers'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'get_github_trending_repositories' beyond the resource type, which is a minor gap in specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as the sibling tool for trending repositories. It lacks any context about scenarios where developers vs. repositories are relevant, leaving usage decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_github_trending_repositoriesC
Get trending repositories on github
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Language to filter repositories by | |
| since | No | Time period to filter repositories by | |
| spoken_language | No | Spoken language to filter repositories by |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Get') but doesn't describe any behavioral traits such as rate limits, authentication requirements, data freshness, or what 'trending' entails (e.g., based on stars, forks). This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and uses minimal words to convey the essential action, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool that fetches trending data with three parameters and no output schema, the description is incomplete. It lacks details on what 'trending' means, the return format, any limitations, or how to interpret results. Without annotations or an output schema, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for all three parameters (language, since, spoken_language). The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('trending repositories on github'), making the purpose immediately understandable. It distinguishes from the sibling tool 'get_github_trending_developers' by specifying repositories rather than developers. However, it doesn't specify what 'trending' means or the scope (e.g., global vs. user-specific), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'get_github_trending_developers' or any other potential tools for GitHub data. There's no context about prerequisites, limitations, or typical use cases, leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
v1.0.0- First observed
get_github_trending_developers - First observed
get_github_trending_repositories
TDQS
The two tools have perfectly distinct purposes: one targets trending developers, the other trending repositories. There is no overlap in functionality, and an agent can easily differentiate between them based on the clear resource distinction.
Both tools follow an identical verb_noun pattern with 'get_github_trending_' prefix, ensuring complete predictability. The naming is highly consistent and readable, with no deviations in style or structure.
With only 2 tools, the server feels thin for its apparent scope of 'github-trending'. While it covers two key resources, the lack of filtering, sorting, or time-range options limits utility, making the count borderline insufficient for robust trending analysis.
The server provides basic access to trending developers and repositories, but there are notable gaps. Missing operations include filtering by language, location, or time period, and there is no way to get historical trending data or detailed analytics, which are common needs in this domain.
Maintenance
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